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multi-objective optimization for Short-Term Scheduling Considering Crude Oil Mixing Requirements Based on Improved NSGA-III

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2025年第0期

作者： Liu, Xianxin Ren, Chuan Shang, Liyan Zhao, Yanfeng Wang, Xiaohua Liaoning Petrochem Univ Coll Petr Engn Fushun Liaoning Peoples R China Natl Petr & Nat Gas Pipeline Network Grp Co Ltd Gansu Branch Lanzhou Gansu Peoples R China Liaoning Petrochem Univ Coll Environm & Safety Engn Fushun Liaoning Peoples R China Liaoning Petrochem Univ Coll Petrochem Engn Fushun Liaoning Peoples R China

This paper addresses the crude oil short-term scheduling problem considering dynamic mixing requirements, aiming to more comprehensively reflect production realities and optimize costs. Based on the given refining schedule at the upper level, a novel multi-objective optimization model is proposed to optimize five key objectives. To solve this model, a new GNSGA-III algorithm is introduced, which incorporates an initialization strategy based on a good point set, along with enhanced crossover and mutation operators to improve population diversity. The results show that the GNSGA-III algorithm outperforms the NSGA-III algorithm, achieving an improvement of 11.6%-26.7% in optimization performance across multiple objectives. Furthermore, the overall scheduling cost is significantly reduced, demonstrating the effectiveness of the proposed approach. The study contributes to optimizing crude oil scheduling by incorporating mixing constraints and improving algorithm efficiency, providing a more cost-effective solution for the practical scheduling problems of refineries.

关键词： crude oil mixing requirements GNSGA-III algorithm good point set multi-objective optimization short-term scheduling

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multi-objective optimization of operational strategy and capacity configuration for hybrid energy system combined with concentrated solar power plant

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APPLIED ENERGY 2025年 390卷

作者： Shuai, Wei Wang, Keqin Zhang, Tian He, Yibin Xu, Haoran Zhu, Peiwang Xiao, Gang Zhejiang Univ State Key Lab Clean Energy Utilizat 38 Zheda Rd Hangzhou 310027 Peoples R China Zhejiang Univ Qingshanhu Energy Res Ctr 1699 Dayuan Rd Hangzhou 311300 Peoples R China Zhejiang Univ Jiaxing Res Inst 1300 Dongshengxilu Rd Jiaxing 314031 Peoples R China

The hybrid energy system (HES) integrated with concentrated solar power (CSP) offers a promising solution for stable power generation. To enhance reliability and cost-effectiveness of HES, this study investigates the operation of next-generation CSP system based on the solar Brayton cycle, within an HES framework. Three operational strategies (continuous operation, scheduled shutdown and predictive operation) are proposed for the HES which consists of a novel CSP plant, wind turbine, photovoltaic system, electric heater, and battery. Performance indicators including the loss of power supply probability (LPSP), the levelized cost of energy (LCOE) and the potential energy waste probability (PEWP) are evaluated to find optimal capacity configuration in different operational strategies. The results show that the predictive operational strategy achieves the lowest LCOE of 0.1866 USD/kWh with an LPSP of 0, which represents 12.6 % reduction compared to the continuous operational strategy when the PEWP constraint is not considered. However, incorporating the PEWP constraint in the multi-objective optimization increases LCOE across all strategies. Under strict LPSP and PEWP constraints, the scheduled shutdown strategy performs the best, with minimal impact from PEWP constraints when LPSP <= 5 %. This work contributes to the improvement of integration and operational efficiency for next-generation renewable energy systems.

关键词： Hybrid energy system Solar air Brayton cycle Operational strategy Capacity configuration multi-objective optimization

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multi-objective optimization of reservoir group operation after Inter-basin water transfer jointing SWAT model and NSGA-II

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JOURNAL OF HYDROLOGY 2025年 660卷

作者： Liu, Ruimin Liu, Yue Jiao, Lijun Wang, Linfang Beijing Normal Univ Sch Environm State Key Lab Water Environm Simulat 19 Xinjiekouwai St Beijing 100875 Peoples R China Shanxi Agr Univ Sorghum Res Inst Shanxi Key Lab Sorghum Genet & Germplasm Innovat Jinzhong 030600 Peoples R China

Inter-basin water transfer (IBWT) projects were widely proposed to solve regional water resource shortages. However, the impact of reservoir group management measures after IBWT on the economy and ecology is often overlooked. In this study, the cyclic coupling of the soil and water assessment tool (SWAT) and the second generation of the Non-dominated Sorting Genetic Algorithm (NSGA-II) was realized. Performance analysis of NSGA-II showed that the best-fit parameters of population size, generations, mutation, and crossover were 100, 50, 0.1, and 0.8, respectively. Based on the coupling model, the optimization of three objectives about economy, resources, and environment and the relationship between discharge flow and objectives were analyzed in the Fenhe River Basin. optimization results showed IBWT project made power generation increase by 88.8%, water shortage decrease by 19%, and water quality improve by 15.43%. There was a non-linear competitive relationship between F2, representing the benefits of water supply, and F1, representing the benefits of hydropower generation, as well as between F2 and F3, representing the benefits of water quality. In this non-linear competitive relationship, the benefits of F1 and F3 brought by unit F2 were reduced to 1/57 and 1/75 of the original respectively. The points that marginal cost of F2 equals marginal benefits of F1 and F3 were served as reference points to balance the benefit among objectives and calculate the optimal total benefit point (1378, 36.49, 36.63) of the three objectives. The driving effects of different objectives on the discharge flow curve were different through the analysis of reservoirs discharge typical plans. The reservoir discharge plan for the best benefit of F3 (F3B) had maximum annual average discharge flow and proportion of discharge flow during flood season to dilute pollutants in the river channel. Meanwhile, the plan for the best benefit of F2 (F2B) had minimum discharge flow to reduce the water

关键词： multi-objective optimization SWAT model NSGA-II algorithm Non-linear competition Marginal benefits

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multi-objective optimization of selective maintenance process considering profitability and personnel energy consumption

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Tian, Guangdong Wang, Miao Yang, Jianwei Mi, Hongjuan Ullah, Saif Aljuaid, Mohammed M. Fathollahi-Fard, Amir M. Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 100044 Peoples R China Yuanda Valve Grp Co Ltd Xingtai 055350 Hebei Peoples R China Univ Engn & Technol Taxila Dept Ind Engn Taxila Pakistan King Saud Univ Coll Business Adm Dept Hlth Adm Riyadh Saudi Arabia Univ Quebec Montreal Dept Analyt Operat & Technol Informat 315Sainte Catherine St East Montreal PQ H2X 3X2 Canada Macao Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau 999078 Peoples R China

Mechanical equipment naturally deteriorates and may malfunction during regular use, resulting in substantial financial losses and downtime. Regular maintenance can effectively address these issues. However, poor maintenance planning for products with numerous components often leads to inefficiencies for maintenance personnel, higher maintenance costs, and unnecessary resource consumption. Selective maintenance helps create effective maintenance programs under resource constraints, scientifically allocate maintenance resources, promptly repair faulty equipment, and sustain production activities. This study develops a multi-objective optimization model to enhance the efficiency of maintenance activities, avoid resource wastage, and increase maintenance revenue. This model optimizes the serial maintenance sequence by considering factors such as maintenance benefits, costs, personnel energy consumption, and resource constraints. Additionally, an improved metaheuristic algorithm, combining brainstorming optimization and large neighborhood search, is proposed to optimize the maintenance scheme for a specific type of carrier booster device system. Finally, an analysis of maintenance practices validates the applicability of the proposed model and algorithm, demonstrating their effectiveness in real-world scenarios.

关键词： Selective maintenance Serial sequence Maintenance planning multi-objective optimization Personnel energy consumption Preventive maintenance Corrective maintenance Resource assignment

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multi-objective optimization of continuous monitoring scheduling for moving targets by earth observation satellites☆

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 144卷

作者： Chao, Tao Han, Xiaofeng Li, Xiang Yang, Ming Harbin Inst Technol Control & Simulat Ctr Harbin 150001 Peoples R China Harbin Inst Technol Sch Math Harbin 150001 Peoples R China

Continuous monitoring scheduling for moving targets by earth observation satellites is a crucial optimization problem in the field of artificial intelligence. In this scenario, for moving targets, extending the observation duration and increasing the capture times are important. However, few studies consider optimizing these two objectives simultaneously. In this paper, we present a novel methodology for this multi-objective optimization scenario. Firstly, a multi-objective optimization model is established. To address the complex Pareto front of the problem, we propose a decomposition-based, weight-adjusted multi-objective evolutionary algorithm that demonstrates strong convergence on the baseline model while further enhancing diversity through weight- adjustment techniques. Experimental results demonstrate that: 1) The proposed method achieves a trade-off solution set that simultaneously balances two objectives, and 2) in comparison to existing multi-objective optimization methods, the proposed algorithm outperforms the existing algorithms in terms of convergence and diversity.

关键词： Earth observation satellites Moving target Scheduling multi-objective optimization Diversity Convergence

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multi-objective optimization of tribological properties of diesel engine camshaft bearings

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STRUCTURAL AND multiDISCIPLINARY optimization 2025年第1期68卷 1-17页

作者： Zhao, Jingjing Li, Yuan Li, Yan Liu, Jinxiang Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Aerosp Prop Inst Beijing 100076 Peoples R China China North Ind Corp Beijing 100053 Peoples R China

In order to improve the tribological performance of camshaft bearings, a design method based on NSGA-II and TOPSIS decision methods was proposed. The structural-performance parameters sample dataset was obtained. The multi-objective optimization genetic algorithm and multi-criteria decision-making method were used to optimize the bearings structure with the goal of minimizing the total friction loss and the maximum wear height, as well as maximizing the average values of minimum oil film thickness. The optimal performance and structural parameters of camshaft bearings obtained through multi-objective optimization strategy have obvious directionality. The entropy weighted TOPSIS multi-criteria decision-making method effectively obtained the optimal solution. Compared with the original structure, the optimized structure significantly reduces the total friction loss and maximum wear height.

关键词： multi-objective optimization Deep neural network Camshaft bearing Friction loss Wear

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multi-objective optimization for layout planning of matrix manufacturing system

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2025年第3期12卷 1049-1067页

作者： Park, Jisoo Lee, Changha Oh, Seog-Chan Do Noh, Sang Sungkyunkwan Univ Dept Ind Engn 2066 Seobu Ro Suwon 16419 South Korea Gen Motors Res & Dev 30500 Mound Rd Warren MI 48090 USA

The automotive industry is experiencing rapid changes due to the rise of the Industry 4.0 manufacturing paradigm, which requires strategic implementation of advanced manufacturing systems to meet diverse customer needs. The Matrix Manufacturing System, characterized by modular facilities and autonomous mobile robots, offers greater flexibility compared to traditional dedicated production systems. This paper conducts a multi-objective optimization of facility layout planning within the matrix manufacturing system to enhance efficiency and responsiveness to market volatility. To solve the optimization problem, three heuristic algorithms-Simulated Annealing, Particle Swarm optimization, and Non-dominated Sorting Genetic Algorithm-II are employed and their performance is compared. For the comparative analysis, frequency maps are used, visualizing the optimization processes and outcomes between metaheuristic algorithms. The framework with methodologies presented in this report is expected to improve productivity and flexibility of a matrix manufacturing system in the automotive industry.

关键词： Matrix manufacturing system Layout planning multi-objective optimization Metaheuristic algorithm Discrete event simulation

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multi-objective optimization framework for nitrogen-containing compounds generation in nitrogen-enriched pyrolysis: Integrating transfer learning and experimental validation

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JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS 2025年 189卷

作者： Wang, Hui Bi, Dongmei Qian, Qingqing Pan, Lei Liu, Shanjian Yi, Weiming Shandong Univ Technol Sch Agr Engn & Food Sci Zibo 255000 Peoples R China Southeast Univ Sch energy & Environm Nanjing 211102 Peoples R China Shandong Univ Technol Branch State Key Lab Woody Oil Resources Utilizat Zibo 255000 Peoples R China Shandong Res Ctr Engn & Technol Clean Energy Zibo 255000 Peoples R China

A multi-objective optimization approach, integrating machine learning and transfer learning, was proposed to optimize the generation of nitrogen-containing compounds in nitrogen-enriched pyrolysis of biomass. A highaccuracy Gradient Boosting Regression Tree (GBRT) model was developed using 827 experimental data sets, with transfer learning employed to accelerate training on specific target variables. This approach significantly enhanced both learning efficiency and predictive performance. The model achieved a Coefficient of Determination (R2) of 0.968 and a Mean Absolute Error (MAE) of 1.047 on the test set, demonstrating exceptional predictive capability. Through Principal Component Analysis (PCA) and model interpretability methods such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), key influencing factors were identified. The critical factors include nitrogen source ratio, pyrolysis temperature, and protective gas. The study identified a synergistic effect when the nitrogen source ratio was 50.00 % and the pyrolysis temperature was 550 degrees C. This condition led to the maximum generation of nitrogen-containing compounds. Additionally, increasing the nitrogen source ratio reduced the formation of volatile compounds, while higher lignin content promoted the formation of aldehydes and ketones. Experimental validation via nitrogenenriched pyrolysis of corn stover confirmed the practical applicability of the model. The model accurately predicted nitrogen-containing compounds generation, with the maximum prediction error constrained to within 6.20 %. This study combines data-driven methods with experimental validation. The approach provides a novel technological framework for optimizing complex chemical reactions and supporting the sustainable production of high-value nitrogen-based chemicals.

关键词： multi-objective optimization Machine learning Transfer learning Nitrogen-containing compounds Pyrolysis conditions

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multi-objective optimization of Low-Velocity Impact and Compression Behavior of 3D-Printed PLA Cubic Samples

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POLYMERS 2025年第5期17卷 627-627页

作者： Dogan, Oguz Kamer, Muhammed S. Sahan, Mehmet F. Kahramanmaras Sutcu Imam Univ Fac Engn & Architecture Dept Mech Engn TR-46040 Kahramanmaras Turkiye Adiyaman Univ Fac Engn Dept Civil Engn TR-02040 Adiyaman Turkiye

This study investigates how various 3D printing parameters influence mechanical properties, specifically strength in compression and low-velocity impact (LVI) tests, and identifies the best printing parameters (layer thickness, nozzle diameter, and infill density) that lead to durable samples. Utilizing a Taguchi L9 orthogonal array, the study systematically examined the effects of three critical 3D printing parameters on the mechanical strength of cubic test samples. Nine experimental configurations were tested, each subjected to compression and LVI tests according to ASTM standards. Statistical analyses, including analysis of variance (ANOVA) and grey relational analysis (GRA), were employed to evaluate parameter significance and optimize results. Infill density significantly influenced the compression tests, while nozzle diameter was the most impactful parameter in LVI tests. Layer thickness had a minimal influence on both outcomes. Additionally, applying GRA revealed that optimal 3D printing parameters differ when considering the two mechanical properties simultaneously, highlighting the complexity of achieving balanced performance in 3D-printed structures. The application of the Taguchi method to optimize 3D printing parameters improved the mechanical properties of printed materials while significantly reducing the number of required experiments. By employing an efficient experimental design, this research demonstrates how to achieve high-quality results in compression and LVI tests with minimal resource use and time investment. Additionally, integrating GRA for the simultaneous optimization of multiple performance characteristics further enhances the practical applicability of the findings in additive manufacturing.

关键词： compression low-velocity impact 3D printing additive manufacturing multi-objective optimization

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multi-objective optimization of variable altitude high-dimensional compression-ignition aviation piston engine based on Kriging model and NSGA-III

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ENERGY 2025年 320卷

作者： Xu, Yuchen Sun, Min Chen, Guisheng Xiao, Renxin Gong, Hang Yang, Jie Yang, Sen Kunming Univ Sci & Technol Yunnan Key Lab Internal Combust Engine Kunming 650500 Peoples R China Kunming Univ Sci & Technol Engn Training Ctr Kunming 650500 Peoples R China Kunming Univ Sci & Technol Coll Civil Aviat & Aeronaut Kunming 650500 Peoples R China

The operation of compression-ignition aviation piston engines in high-altitude environments is prone to critical issues such as power degradation and insufficient thrust. The research and optimization of the rapid coordination mechanism between fuel and air are crucial for preventing power loss during high-altitude operation and improving the service ceiling of the engine. Based on the constructed one-dimensional thermodynamic model of a compression-ignition aviation piston engine (CI APE), the Kriging surrogate model and non-dominated sorting genetic algorithm (NSGA) are utilized to optimize the brake specific fuel consumption (BSFC), maximum pressure rise rate (MPRR), and the maximum cylinder pressure (Pmax), exploring the optimal fuel-air combination at different altitudes. Firstly, the Pearson correlation coefficient analysis method is employed to confirm variables, and Latin hypercube sampling is used to generate training model samples. Secondly, a Kriging surrogate model of the engine with BSFC, MPRR, and Pmax as objective functions is constructed, and its accuracy is validated. Finally, the NSGA-III is employed for multi-objective optimization. The results indicate that injection timing, compression ratio, high-pressure stage blade opening, and low-pressure stage blade opening have the most significant impact on engine performance. The constructed surrogate models exhibit good predictive accuracy, with coefficient of determination (R2) values all greater than 0.9. At altitudes of 2000 m, 4000 m, 6000 m, and 8000 m, compared to before optimization, the BSFC decreased by 10.1 %, 11.5 %, 12.7 %, and 12 %, respectively. Compared to the power at 2000 m altitude before optimization, the optimized engine can achieve approximately 85 % of the power recovery target at 8000 m altitude.

关键词： Compression-ignition aircraft piston engine Kriging surrogate model NSGA-III algorithm multi-objective optimization

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